Chapter 8 First Order Logic - Hacettepe

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CS461 Artificial Intelligence © Pinar Duygulu Spring

2008

First Order Logic

Artificial Intelligence

Slides are mostly adapted from AIMA and MIT Open Courseware,

and Milos Hauskrecht (U. Pittsburgh)

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Pros and cons of propositional logic

Propositional logic is declarative

Propositional logic allows partial/disjunctive/negated information– (unlike most data structures and databases)

Propositional logic is compositional:– meaning of B1,1 P1,2 is derived from meaning of B1,1 and of P1,2

Meaning in propositional logic is context-independent– (unlike natural language, where meaning depends on context)

Propositional logic has very limited expressive power– (unlike natural language)

– E.g., cannot say "pits cause breezes in adjacent squares“

• except by writing one sentence for each square

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First Order Logic

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First-order logic

• First-order logic (like natural language) assumes the world contains

– Objects: people, houses, numbers, colors, baseball games, wars, …

– Relations: red, round, prime, brother of, bigger than, part of, comes between, …

– Functions: father of, best friend, one more than, plus,

• (relations in which there is only one value for a given input)

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FOL Motivation

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Syntax of FOL: Basic elements

• Constants : KingJohn, 2, ...

• Predicates: Brother, >,...

• Functions : Sqrt, LeftLegOf,...

• Variables x, y, a, b,...

• Connectives , , , ,

• Equality =

• Quantifiers ,

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Atomic sentences

Term = function (term1,...,termn)

or constant

or variable

Atomic sentence = predicate (term1,...,termn)

or term1 = term2

• E.g., Brother(KingJohn,RichardTheLionheart)

• > (Length(LeftLegOf(Richard)), Length(LeftLegOf(KingJohn)))

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Complex sentences

Complex sentences are made from atomic sentences using

connectives

S, S1 S2, S1 S2, S1 S2, S1 S2,

E.g.

Sibling(KingJohn,Richard) Sibling(Richard,KingJohn)

>(1,2) ≤ (1,2)

>(1,2) >(1,2)

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Quantifiers

Universal quantification, (pronounced as “For all”)

x Cat(x) Mammal(x)

All cats are mammals

Existential quantification, (pronounced as “There exists”)

x Sister (x, Spot) Cat(x)

Spot has a sister who is a cat

• x P is true in a model m

iff P is true with x being each possible object in the model

• x P is true in a model m

iff P is true with x being some possible object in the model

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Truth in first-order logic

• Sentences are true with respect to a model and an interpretation

• Model contains objects (domain elements) and relations among them

• An atomic sentence predicate(term1,...,termn) is true

iff the objects referred to by term1,...,termn

are in the relation referred to by predicate

• Interpretation specifies referents for

constant symbols → objects

predicate symbols → relations

function symbols → functional relations

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Interpretation

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Models for FOL: Example

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Semantics

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Semantics

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Universal quantification

<variables> <sentence>

All Kings are persons:

x King(x) Person(x)

x P is true in a model m

iff P is true with x being each possible object in the model

Roughly speaking, equivalent to the conjunction of instantiations of P

Richard the Lionheart is a king Richard the Lionheart is a person

King John is a king King John is a person

Richard’s left leg is a king Richard’s left leg is a person

John’s left leg is a king John’s left leg is a person

The crown is a king The crown is a person

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A common mistake to avoid

• Typically, is the main connective with

x King(x) Person(x)

• Common mistake: using as the main connective with :

x King(x) Person(x)

means “Everyone is a king and everyone is a person”

Richard the Lionheart is a king Richard the Lionheart is a person

King John is a king King John is a person

Richard’s left leg is a king Richard’s left leg is a person

John’s left leg is a king John’s left leg is a person

The crown is a king The crown is a person

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Existential quantification

<variables> <sentence>

x Crown(x) OnHead(x,John)

x P is true in a model m

iff P is true with x being some possible object in the model

Roughly speaking, equivalent to the disjunction of

instantiations of P

The crown is a crown the crown is on John’s head

Richard the Lionheart is a crown Richard the Lionheart is on John’s head

King John is a crown King John is on John’s head

...

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Another common mistake to avoid

• Typically, is the main connective with

• x Crown(x) OnHead(x,John)

• Common mistake: using as the main connective with :

x Crown(x) OnHead(x,John)

is true even if there is anything which is not a crown

The crown is a crown the crown is on John’s head

Richard the Lionheart is a crown Richard the Lionheart is on John’s head

King John is a crown King John is on John’s head

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Properties of quantifiers

x y is the same as y x, and can be written as x,y

x y is the same as y x , and can be written as x,y

x y is not the same as y x

y x Loves(x,y)– “Everyone in the world is loved by at least one person”

x y Loves(x,y)– “There is a person who loves everyone in the world”

x y P(x,y) : every object in the universe has a particular property, given by P

x y P(x,y) : there is some object in the world that has a particular property

Rule: the variable belongs to the innermost quantifier that mentions it

x [Cat(x) V (x Brother(Richard,x))]

x [Cat(x) V (z Brother(Richard,z))]

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Properties of quantifiers

• Quantifier duality: each can be expressed using the other

x Likes(x,Broccoli) = x Likes(x,Broccoli)

x Likes(x,IceCream) = x Likes(x,IceCream)

• De Morgan’s rules for quantifiers:

x P = x P

x P = x P

x P = x P

x P = x P

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Equality

• term1 = term2 is true under a given interpretation if

and only if term1 and term2 refer to the same object

• E.g., definition of Sibling in terms of Parent:

x,y Sibling(x,y) [(x = y) m,f (m = f)

Parent(m,x) Parent(f,x) Parent(m,y) Parent(f,y)]

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Writing FOL

There is somebody who is loved by everybody

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Writing FOL

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Using FOL

The kinship domain:

• Brothers are siblings

x,y Brother(x,y) Sibling(x,y)

• One's mother is one's female parent

m,c Mother(c) = m (Female(m) Parent(m,c))

• “Sibling” is symmetric

x,y Sibling(x,y) Sibling(y,x)

• One's husband is one's male spouse

w,h Husband(h,w) (Male(m) Spouse(h,w))

• Sibling is another child of one’s parents

x,y Sibling(x,y) [(x = y) m,f (m = f) Parent(m,x)

Parent(f,x) Parent(m,y) Parent(f,y)]

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Inference in

First Order Logic


Slides are mostly adapted from AIMA and MIT Open Courseware,

Milos Hauskrecht (U. Pittsburgh)

and Max Welling (UC Irvine)

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Logical Inference

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Inference in Propositional Logic

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Inference in FOL : Truth Table Approach

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Inference Rules

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Sentences with variables

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Sentences with variables

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Variable Substitutions

SUBST(θ, α)θ =

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Universal elimination

• Every instantiation of a universally quantified sentence is entailed by it:

v αSubst({v/g}, α)

for any variable v and ground term g

• E.g., x King(x) Greedy(x) Evil(x) yields:

King(John) Greedy(John) Evil(John), {x/John}

King(Richard) Greedy(Richard) Evil(Richard), {x/Richard}

King(Father(John)) Greedy(Father(John)) Evil(Father(John)),

{x/Father(John)}

.

.

{x/Ben}

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Existential elimination

• For any sentence α, variable v, and constant symbol k that does notappear elsewhere in the knowledge base:

v α

Subst({v/k}, α)

• E.g., x Crown(x) OnHead(x,John) yields:

Crown(C1) OnHead(C1,John)

provided C1 is a new constant symbol, called a Skolem constant

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Inference rules for quantifiers

αv Subst({g/v}, α)

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Example Proof

• The law says that it is a crime for an American to sell weapons to hostile nations. The country Nono, an enemy of America, has some missiles, and all of its missiles were sold to it by Colonel West, who is American.

• Prove that Col. West is a criminal

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Example knowledge base contd.

... it is a crime for an American to sell weapons to hostile nations:

x,y,z American(x) Weapon(y) Sells(x,y,z) Nation(z) Hostile(z) Criminal(x)

Nono … has some missiles, i.e.,

x Owns(Nono,x) Missile(x):

… all of its missiles were sold to it by Colonel West

x Missile(x) Owns(Nono,x) Sells(West,x,Nono)

Missiles are weapons:

x Missile(x) Weapon(x)

An enemy of America counts as "hostile“:

x Enemy(x,America) Hostile(x)

West, who is American …

American(West)

The country Nono

Nation(Nono)

Nono, an enemy of America …

Enemy(Nono,America), Nation(America)

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Example knowledge base contd.

1. x,y,z American(x) Weapon(y) Sells(x,y,z) Nation(z) Hostile(z) Criminal(x)

2. x Owns(Nono,x) Missile(x):

3. x Missile(x) Owns(Nono,x) Sells(West,x,Nono)

4. x Missile(x) Weapon(x)

5. x Enemy(x,America) Hostile(x)

6. American(West)

7. Nation(Nono)

8. Enemy(Nono,America)

9. Nation(America)

10. Owns(Nono,M1) and Missile(M1) Existential elimination 2

11. Owns(Nono,M1) And elimination 10

12. Missile(M1) And elimination 10

13. Missile(M1) Weapon(M1) Universal elimination 4

14. Weapon(M1) Modus Ponens, 12, 13

15. Missile(M1) Owns(Nono,M1) Sells(West,M1,Nono) Universal Elimination 3

16. Sells(West,M1,Nono) Modus Ponens 10,15

17. American(West) Weapon(M1) Sells(West,M1,Nono) Nation(Nono) Hostile(Nono) Criminal(Nono) Universal elimination, three times 1

18. Enemy(Nono,America) Hostile(Nono) Universal Elimination 5

19. Hostile(Nono) Modus Ponens 8, 18

20. American(West) Weapon(M1) Sells(West,M1,Nono) Nation(Nono) Hostile(Nono) And Introduction 6,7,14,16,19

21. Criminal(West) Modus Ponens 17, 20

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Reduction to propositional inference

Suppose the KB contains just the following:

x King(x) Greedy(x) Evil(x)

King(John)

Greedy(John)

Brother(Richard,John)

• Instantiating the universal sentence in all possible ways (there are only two ground terms: John and Richard) , we have:

King(John) Greedy(John) Evil(John)

King(Richard) Greedy(Richard) Evil(Richard)

King(John)

Greedy(John)


• The new KB is propositionalized: proposition symbols are

King(John), Greedy(John), Evil(John), King(Richard), etc.

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Reduction contd.

• Every FOL KB can be propositionalized so as to preserve

entailment– A ground sentence is entailed by new KB iff entailed by original KB

• Idea for doing inference in FOL:– propositionalize KB and query

– apply inference

– return result

• Problem: with function symbols, there are infinitely many

ground terms,– e.g., Father(Father(Father(John))), etc

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Reduction contd.

Theorem: Herbrand (1930). If a sentence α is entailed by an FOL KB, it is entailed by a finite subset of the propositionalized KB

Idea: For n = 0 to ∞ do

create a propositional KB by instantiating with depth-n terms

see if α is entailed by this KB

Problem: works if α is entailed, loops if α is not entailed

Theorem: Turing (1936), Church (1936)

Entailment for FOL is semidecidable (algorithms exist that say yes to every entailed sentence, but no algorithm exists that also says no to every nonentailed sentence.)

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Problems with propositionalization

• Propositionalization seems to generate lots of irrelevant sentences.

• E.g., from:

x King(x) Greedy(x) Evil(x)

King(John)

y Greedy(y)


• it seems obvious that Evil(John) is entailed, but propositionalization produces lots of facts such as Greedy(Richard) that are irrelevant

• With p k-ary predicates and n constants, there are p·nk instantiations.

• Lets see if we can do inference directly with FOL sentences

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Generalized Modus Ponens (GMP)

p1', p2', … , pn', ( p1 p2 … pn q)

Subst(θ,q)

Example:

p1' is King(John) p1 is King(x)

p2' is Greedy(y) p2 is Greedy(x)

θ is {x/John, y/John} q is Evil(x)

Subst(θ,q) is Evil(John)

Example:

p1' is Missile(M1) p1 is Missile(x)

p2' is Owns(y, M1) p2 is Owns(Nono,x)

θ is {x/M1, y/Nono} q is Sells(West, Nono, x)

Subst(θ,q) is Sells(West, Nono, M1)

• Implicit assumption that all variables universally quantified

where we can unify pi‘ and pi for all i

i.e. pi'θ = pi θ for all i

GMP used with KB of definite clauses (exactly one positive literal)

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Soundness and completeness of GMP

• Need to show that p1', …, pn', (p1 … pn q) ╞ qθ

provided that pi'θ = piθ for all I

• Lemma: For any sentence p, we have p ╞ pθ by UI

1. (p1 … pn q) ╞ (p1 … pn q)θ = (p1θ … pnθ qθ)

2. p1', \; …, \;pn' ╞ p1' … pn' ╞ p1'θ … pn'θ

3. From 1 and 2, qθ follows by ordinary Modus Ponens

GMP is sound

Only derives sentences that are logically entailed

GMP is complete for a KB consisting of definite clauses– Complete: derives all sentences that entailed

– OR…answers every query whose answers are entailed by such a KB

–

– Definite clause: disjunction of literals of which exactly 1 is positive,

e.g., King(x) AND Greedy(x) -> Evil(x)

NOT(King(x)) OR NOT(Greedy(x)) OR Evil(x)

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Generalized Modus Ponens (GMP)

p1', p2', … , pn', ( p1 p2 … pn q)

Subst(θ,q)

Convert each sentence into cannonical form prior to inference:

Either an atomic sentence or an implication with a conjunction of

atomic sentences on the left hand side and a single atom on the right

(Horn clauses)

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Unification

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Unification

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Unification

• We can get the inference immediately if we can find a substitution θ such that King(x) and Greedy(x) match King(John) and Greedy(y)

θ = {x/John,y/John} works

• Unify(α,β) = θ if αθ = βθ

p q θ

Knows(John,x) Knows(John,Jane) {x/Jane}}

Knows(John,x) Knows(y, Elizabeth) {x/ Elizabeth,y/John}}

Knows(John,x) Knows(y,Mother(y)) {y/John,x/Mother(John)}}

Knows(John,x) Knows(x, Elizabeth) {fail}

• Standardizing apart eliminates overlap of variables,e.g., Knows(z17, Elizabeth)

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Unification

• To unify Knows(John,x) and Knows(y,z),

θ = {y/John, x/z } or θ = {y/John, x/John, z/John}

• The first unifier is more general than the second.

• Most general unifier is the substitution that makes the least commitment about the bindings of the variables

• There is a single most general unifier (MGU) that is unique up to renaming of variables.

MGU = { y/John, x/z }

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The unification algorithm

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The unification algorithm

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Example knowledge base revisited1. x,y,z American(x) Weapon(y) Sells(x,y,z) Nation(z) Hostile(z) Criminal(x)

2. x Owns(Nono,x) Missile(x):

3. x Missile(x) Owns(Nono,x) Sells(West,x,Nono)

4. x Missile(x) Weapon(x)

5. x Enemy(x,America) Hostile(x)

6. American(West)

7. Nation(Nono)


9. Nation(America)

Convert the sentences into Horn form

1. American(x) Weapon(y) Sells(x,y,z) Nation(z) Hostile(z) Criminal(x)

2. Owns(Nono,M1)

3. Missile(M1)

4. Missile(x) Owns(Nono,x) Sells(West,x,Nono)

5. Missile(x) Weapon(x)

6. Enemy(x,America) Hostile(x)

7. American(West)

8. Nation(Nono)


10. Nation(America)

11. Proof

12. Weapon(M1)

13. Hostile(Nono)

14. Sells(West,M1,Nono)

15. Criminal(West)

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Inference appoaches in FOL

• Forward-chaining

– Uses GMP to add new atomic sentences

– Useful for systems that make inferences as information streams in

– Requires KB to be in form of first-order definite clauses

• Backward-chaining

– Works backwards from a query to try to construct a proof

– Can suffer from repeated states and incompleteness

– Useful for query-driven inference

• Note that these methods are generalizations of their propositional equivalents

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Forward chaining algorithm

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Forward chaining proof

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Properties of forward chaining

• Sound and complete for first-order definite clauses

• Datalog = first-order definite clauses + no functions

• FC terminates for Datalog in finite number of iterations

• May not terminate in general if α is not entailed

• This is unavoidable: entailment with definite clauses is semidecidable

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Efficiency of forward chaining

Incremental forward chaining: no need to match a rule on iteration k if a premise wasn't added on iteration k-1

match each rule whose premise contains a newly added positive literal

Matching itself can be expensive:

Database indexing allows O(1) retrieval of known facts

– e.g., query Missile(x) retrieves Missile(M1)

Forward chaining is widely used in deductive databases

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Backward chaining algorithm

SUBST(COMPOSE(θ1, θ2), p) = SUBST(θ2, SUBST(θ1, p))

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Backward chaining example

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Properties of backward chaining

• Depth-first recursive proof search: space is linear in size of proof

• Incomplete due to infinite loops

– fix by checking current goal against every goal on stack

• Inefficient due to repeated subgoals (both success and failure)

– fix using caching of previous results (extra space)

• Widely used for logic programming

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Logic programming: Prolog

• Algorithm = Logic + Control

• Basis: backward chaining with Horn clauses + bells & whistles

• Program = set of clauses = head :- literal1, … literaln.

criminal(X) :- american(X), weapon(Y), sells(X,Y,Z), hostile(Z).

• Depth-first, left-to-right backward chaining

• Built-in predicates for arithmetic etc., e.g., X is Y*Z+3

• Built-in predicates that have side effects (e.g., input and output

• predicates, assert/retract predicates)

• Closed-world assumption ("negation as failure")

– e.g., given alive(X) :- not dead(X).

– alive(joe) succeeds if dead(joe) fails

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Resolution in

First Order Logic


Slides are mostly adapted from AIMA and MIT Open Courseware

and Milos Hauskrecht (U. Pittsburgh)

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Resolution Inference Rule

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First Order Resolution

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Clausal Form

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Converting to Clausal Form

Also move all quantifiers left

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Converting to Clausal Form - Skolemization

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Converting to Clausal Form - Skolemization

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Converting to Clausal Form

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Inference with resolution rule

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Dealing with Equality

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Example

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More examples

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First Order Resolution

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Substitutions

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Unification

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Most General Unifier

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Unification Algorithm

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Unify-var subroutine

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Examples

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Resolution with Variables

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Curiosity Killed the Cat

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Proving Validity

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Example

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Example

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Green’s Trick

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Equality

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Proof Example

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The Clauses

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The Query

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The Proof

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Hat of D

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Who is Jane’s Lower

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Chapter 8 First Order Logic - Hacettepe